smacofx (version 1.6-1)

clca: Curvilinear Component Analysis (CLCA)

Description

A wrapper to run curvilinear component analysis via CCA and returning a 'smacofP' object. Note this functionality is rather rudimentary.

Usage

clca(
  delta,
  Epochs = 20,
  alpha0 = 0.5,
  lambda0,
  ndim = 2,
  weightmat = 1 - diag(nrow(delta)),
  init = NULL,
  acc = 1e-06,
  itmax = 10000,
  verbose = 0,
  method = "euclidean",
  principal = FALSE
)

Value

a 'smacofP' object. It is a list with the components

• delta: Observed, untransformed dissimilarities

• tdelta: Observed explicitly transformed dissimilarities, normalized

• dhat: Explicitly transformed dissimilarities (dhats), optimally scaled and normalized

• confdist: Configuration dissimilarities

• conf: Matrix of fitted configuration

• stress: Default stress (stress-1; sqrt of explicitly normalized stress)

• spp: Stress per point

• ndim: Number of dimensions

• model: Name of model

• niter: Number of iterations (training length)

• nobj: Number of objects

• type: Type of MDS model. Only ratio here.

• weightmat: weighting matrix as supplied

• stress.m: Default stress (stress-1^2)

• tweightmat: transformed weighting matrix; it is weightmat here.

Arguments

delta

dist object or a symmetric, numeric data.frame or matrix of distances.

Epochs

Scalar; gives the number of passes through the data.

alpha0

(scalar) initial step size, 0.5 by default

lambda0

the boundary/neighbourhood parameter(s) (called lambda_y in the original paper). It is supposed to be a numeric scalar. It defaults to the 90% quantile of delta.

ndim

dimension of the configuration; defaults to 2

weightmat

not used

init

starting configuration, not used

acc

numeric accuracy of the iteration; not used

itmax

maximum number of iterations. Not used.

verbose

should iteration output be printed; not used

method

Distance calculation; currently not used.

principal

If 'TRUE', principal axis transformation is applied to the final configuration

Details

This implements CCA as in Demartines & Herault (1997). A different take on the ideas of curvilinear compomnent analysis is available in the experimental functions spmds and spmds.

Examples

dis<-smacof::morse
res<-clca(dis,lambda0=0.4)
res
summary(res)
plot(res)